OPTIMAL TARGET TRAJECTORY ESTIMATION AND FILTERING USING NETWORKED SENSORS

Target tracking using distributed sensor network is in general a challenging problem because it always needs to deal with real-time processing of noisy information. In this paper the problem of using nonlinear sensors such as distance and direction sensors for estimating a moving target is studied. The problem is formulated as a prudent design of nonlinear filters for a linear system subject to noisy nonlinear measurements and partially unknown input, which is generated by an exogenous system. In the worst case where the input is completely unknown, the exogenous dynamics is reduced to the random walk model. It can be shown that the nonlinear filter will have optimal convergence if the number of the sensors are large enough and the convergence rate will be highly improved if the sensors are deployed appropriately. This actually raises an interesting issue on active sensing： how to optimally move the sensors if they are considered as mobile multi-agent systems？ Finally, a simulation example is given to illustrate and validate the construction of our filter.     

Data-Driven Fitting of the G/G/1 Queue

The Maximum Likelihood Estimation(MLE)method is an established statistical method to estimate unknown parameters of a distribution.A disadvantage of the MLE method is that it requires an analytically tractable density,which is not available in many cases.This is the case,for example,with applications in service systems,since waiting models from queueing theory typically have no closed-form solution for the underlying density.This problem is addressed in this paper.MLE is used in combination with Stochastic Approximation(SA)to calibrate the arrival parameterθof a G/G/1 queue via waiting time data.Three different numerical examples illustrate the application of the proposed estimator.Data sets of an M/G/1 queue,G/M/1 queue and model mismatch are considered.In a model mismatch,a mismatch is present between the used data and the postulated queuing model.The results indicate that the estimator is versatile and can be applied in many different scenarios.     

Complexity and intransitivity in technological development

This paper investigates the existence of a fundamental link between two disciplines that emerged during last few decades： complexity science and advanced engineering. During this time many industries, especially those related to the high-tech end of technological development, have faced the problem of increasing complexity of design, production and operation. Industrial projects have grown to become multidisciplinary, tightly interconnected, costly and difficult to control and predict. Two trends can be identified in this respect： one is the consistent effort of systems engineering in reducing the uncertainties of complex industrial operations and the other is the effort undertaken in complexity studies to account for uncertainties present in the real world.
In this work, we provide a brief overview of recent developments in advanced engineering and give a consistent interpretation of technological evolution from the perspective of complexity science in general and complex competitive systems （CCS） in particular. CCS is a general framework that was recently developed for analysis of complex systems involving competition. Transitivity of the decision-making process and the cyclic nature of technological progress are considered. Correctness of intransitive decisions is inherently relativistic： the same decisions can be seen as correct or incorrect when considered from different perspectives. When treated simplistically, intransitivity may seem to be illogical but, nevertheless, it is common in nature and needs to be studied. CCS provides a formalised scientific framework for analysis of intransitivity and establishes the existence of an important connection linking complexity and uncertainty with intransitivity. Implications of intransitivity for engineering decision-making and strategic planning are considered in the context of CCS. A working example of intransitivity in competition between major car mantifacturers is presented.     

SOFT CONTROL ON COLLECTIVE BEHAVIOR OF A GROUP OF AUTONOMOUS AGENTS BY A SHILL AGENT

This paper asks a new question： how can we control the collective behavior of self-organized multi-agent systems？ We try to answer the question by proposing a new notion called ＇Soft Control＇ which keeps the local rule of the existing agents in the system. We show the feasibility of soft control by a case study. Consider the simple but typical distributed multi-agent model proposed by Vicsek et al. for flocking of birds： each agent moves with the same speed but with different headings which are updated using a local rule based on the average of its own heading and the headings of its neighbors. Most studies of this model are about the self-organized collective behavior, such as synchronization of headings. We want to intervene in the collective behavior （headings） of the group by soft control. A specified method is to add a special agent, called a ＇Shill＇, which can be controlled by us but is treated as an ordinary agent by other agents. We construct a control law for the shill so that it can synchronize the whole group to an objective heading. This control law is proved to be effective analytically and numerieally. Note that soft control is different from the approach of distributed control. It is a natural way to intervene in the distributed systems. It may bring out many interesting issues and challenges on the control of complex systems.     

Computational intelligence approach for uncertainty quantification using evidence theory

As an alternative or complementary approach to the classical probability theory,the ability of the evidence theory in uncertainty quantification(UQ) analyses is subject of intense research in recent years.Two state-of-the-art numerical methods,the vertex method and the sampling method,are commonly used to calculate the resulting uncertainty based on the evidence theory.The vertex method is very effective for the monotonous system,but not for the non-monotonous one due to its high computational errors.The sampling method is applicable for both systems.But it always requires a high computational cost in UQ analyses,which makes it inefficient in most complex engineering systems.In this work,a computational intelligence approach is developed to reduce the computational cost and improve the practical utility of the evidence theory in UQ analyses.The method is demonstrated on two challenging problems proposed by Sandia National Laboratory.Simulation results show that the computational efficiency of the proposed method outperforms both the vertex method and the sampling method without decreasing the degree of accuracy.Especially,when the numbers of uncertain parameters and focal elements are large,and the system model is non-monotonic,the computational cost is five times less than that of the sampling method.     

ROBUST CONSENSUS FOR MULTI-AGENT SYSTEMS OVER UNBALANCED DIRECTED NETWORKS

In this paper, a distributed consensus protocol is proposed for discrete-time single-integer multi-agent systems with measurement noises under general fixed directed topologies, The time-varying control gains satisfying the stochastic approximation conditions are introduced to attenuate noises, thus the closed-loop multi-agent system is intrinsically a linear time-varying stochastic difference system. Then the mean square consensus convergence analysis is developed based on the Lyapunov technique, and the construction of the Lyapunov function especially does not require the typical balanced network topology condition assumed for the existence of quadratic Lyapunov function. Thus, the proposed consensus protocol can be applicable to more general networked multi-agent systems, particularly when the bidirectional and/or balanced information exchanges between agents are not required. Under the proposed protocol, it is proved that the state of each agent converges in mean square to a common random variable whose mathematical expectation is the weighted average of agents＇ initial state values; meanwhile, the random variable＇s variance is bounded.     

SCHEDULING TWO GROUPS OF JOBS WITH INCOMPLETE INFORMATION

In real world situations, most scheduling problems occur neither as complete off-line nor ascomplete on-line models. Most likely a problem arises as an on-line model with some partialinformation. In this article, we consider such a model. We study the scheduling problem P(n_1,n_2),where two groups of jobs are to be scheduled. The first job group is available beforehand. As soon asall jobs in the first group are assigned, the second job group appears. The objective is to minimize thelongest job completion time(makespan). We show a lower bound of 3/2 even for very special cases.Best possible algorithms are presented for a number of cases. Furthermore, a heuristic is proposed forthe general case. The main contribution of this paper is to discuss the impact of the quantity ofavailable information in designing an on-line algorithm. It is interesting to note that the absence ofeven a little bit information may significantly affect the performance of an algorithm.     

Validation methodology for distribution-based degradation model

Distribution-based degradation models (or graphical approach in some literature) occur in a wide range of applications. However, few of existing methods have taken the validation of the built model into consideration. A validation methodology for distribution-based models is proposed in this paper. Since the model can be expressed as consisting of assumptions of model structures and embedded model parameters, the proposed methodology carries out the validation from these two aspects. By using appropriate statistical techniques, the rationality of degradation distributions, suitability of fitted models and validity of degradation models are validated respectively. A new statistical technique based on control limits is also proposed, which can be implemented in the validation of degradation models’ validity. The case study on degradation modeling of an actual accelerometer shows that the proposed methodology is an effective solution to the validation problem of distribution-based degradation models.     

考虑随机权重阈值的多态k/N系统任务可用度建模与优化

在权重阈值随机变化的条件下,现有多态系统可用度建模在实际工程应用中具有局限性.本文引入数量阈值并考虑权重阈值随机性,分析多态k/N系统中部件数量和权重不能彼此映射的问题,提出新的随机联合运算符结合通用生成函数的方法,建立系统任务可用度评价模型.本方法适用于权重阈值服从连续型随机分布的情形,优于传统递归算法.然后,分别构建系统可用性约束或成本约束下的系统优化模型,通过遗传算法编程,求解部件各状态的最优概率及权重.最后,以船舶运输系统为例阐述数量阈值对系统可用度的影响,并给出随机权重阈值下多态系统的设计优化结果.研究成果可为系统工程师开展可靠性设计和改进提供方法依据.     

The Similar Structures and Control Problems of Complex Systems

1 Introduction1 We consider the naturally evolving complex systems, such as biotic and social systems. In their evolving process, they always make up their weaknesses and develop their favorable factors. In such a way, their structures are gradually formed. In the structures of these systems, a feature is noteworthy, i.e., the local(partial) structure is similar to the global(overall). For social systems, for instance, in a country, the management organization of a province is similar to tha…     

非同质平衡子系统整体效率的DEA测度与分解

分别在可变规模收益和固定规模收益假设下,构建包含非同质平衡子系统的一类复杂生产系统的DEA效率测度模型,并对整体效率进行满足凸线性组合的分解。与现有的研究相比,建模方法更具有一般性,允许子系统具有不同的生产要素组合以及不同的生产要素权重结构;且易被扩展,用来测度包含更加复杂生产结构的多种生产系统的效率。最后用新建模型对中国高技术产业创新投资行为的有效性进行测度和比较。     

Granularity as a determinant of system architecture

The size of unit components in general systems is claimed to be the major determinant of architecture. Three generations of systems are characterized in terms of their component size. Architectural issues in information systems, production management, and organizational structure are identified and related to the size of their unit components. Historical debates on classification of information systems, production systems, and organizational structures are reviewed, analyzed, and explained in terms of three general architectural classes based on a selection of a unit component and the size of that unit component.     

基于相互作用网络的大型工程项目组织结构风险分析

为解决在工程项目管理中将组织和项目隔离的问题, 提出了大型工程项目系统的组织-任务相互作用网络模型. 在随机失效和故意攻击的条件下, 分别对随机型、集权型和金字塔型组织网络结构对项目工期的影响进行了解析和仿真分析. 研究结果表明: 只需攻击一定比例的组织节点即可导致整个网络完全瘫痪, 从而造成工期严重延误. 当组织中较少比例节点遭受故意攻击时, 项目工期将明显多于同样比例节点存在随机失效的情况. 以广州-珠海城际轨道交通工程项目为例, 构建了该项目的相互作用网络并对其组织节点面临随机失效和故意攻击的情况下进行了风险分析.     

STOCHASTIC ADAPTIVE SWITCHING CONTROL BASED ON MULTIPLE MODELS

1 IntroductionIn an uncertain and complex environment, the approach of "optimal" switching is often usedfor making decisions through predicting and comparing the effects of multiple schemes. In thearea of control, the mu1tipIe model approach, which has been used to improve estimations andcontrol accuracies, can be traced back at least to 1960s--1970s(see, e.g., [1l--[3l). Some practicalapplications have also been reported[4,']. In adaptive control, switching cootroller based onmultiple models…     

On invariant checking

Checking whether a given formula is an invariant at a given program location (especially, inside a loop) can be quite nontrivial even for simple loop programs, given that it is in general an undecidable property. This is especially the case if the given formula is not an inductive loop invariant, as most automated techniques can only check or generate inductive loop invariants. In this paper, conditions are identified on simple loops and formulas when this check can be performed automatically. A general theorem is proved which gives a necessary and sufficient condition for a formula to be an invariant under certain restrictions on a loop. As a byproduct of this analysis, a new kind of loop invariant inside the loop body, called inside-loop invariant, is proposed. Such an invariant is more general than an inductive loop invariant typically used in the Floyd-Hoare axiomatic approach to program verification. The use of such invariants for program debugging is explored; it is shown that such invariants can be more useful than traditional inductive loop invariants especially when one is interested in checking extreme/side conditions such as underflow, accessing array/collection data structures outside the range, divide by zero, etc.     

探索复杂性是发展系统学的重要途径

系统学作为系统科学的基础科学,关注系统演化、结构和功能的一般规律.近年来复杂性研究的发展表明,复杂系统的涌现性是系统学研究中的核心科学问题之一.复杂系统的涌现性通常是指系统通过个体之间的非线性相互作用,可以在宏观层次上出现新的时空结构和功能.通过对各个领域复杂系统宏观涌现行为的研究,抽象出具有普适性的一般规律,并把这样的一般规律用于具体系统的研究,是认识系统复杂性、发展系统学的基本途径.     

随机型时间序列预测方法的研究

提出了一种随机型时间序列预测的新方法，特别适用于趋势随机型数据序列的预测问题，与现有预测方法相比，具有计算简单、精度高的特点.